Query expansion is an effective approach to enrich the original oral query. Some researches consider the correlations between a query and its expansion candidates to select the most relevant candidate from the candidate pool: [1]} proposes a query expansion method based on probabilistic correlations between query logs; [2]} uses a DNN model to directly predict the scores for query expansion terms. Both approaches make query more complete but do not consider the weighting between original query and expanded ones. Other works have studied how to directly generate the expansions: [3]} proposes a concept-based translation method to generate the expanded query; [4]} uses BERT [5]} and BART [6]} with a next query prediction task to do query suggestion; docT5query [7]} implements a powerful T5 model [8]} to predict the relevant query for each document. The generative approaches are able to provide large number of variants, but it may also bring noise and lead to incorrect reformulations.
